A random mix from the whole library. Shuffle for a new set.
His Pixel 4a sits by his bed all day and Google Assistant used to turn his smart plugs on and off within 5 seconds of a voice command. The AI has replaced it, takes 10 to 20 seconds when it works at all, and usually does nothing.
Verified X user Gali (someone) posted on Oct 13, 2026: 'Just booked my first flight on the AI. Time: 18 minutes.
Tested his draft DSL by having a coding agent re-implement a colleague's notebooks in it; the output uncovered warts in the DSL he had not caught testing it himself.
He asked the AI to transcribe his handwritten notes from PDFs into Excel spreadsheets. It started doing it, then gave up.
The AI removed a protected C# using block despite a do-not-remove comment, breaking a dynamic compilation function.
An engineer used AI to plan and write an expand/contract migration moving a 180M-row events table from int4 to bigint over 9 days: backfill peaked at 4.1 seconds of replica lag and the final swap locked for 1.4 seconds, with zero downtime. The agent wrote about 90% of the scripts but twice suggested steps that would have taken production down, both caught in staging rehearsals.
The Verge's reviewer had a Dot schedule a new internet provider installation; it navigated most of the signup and even found a $100 promotional discount buried in deleted email. It stalled at a press-and-hold human check its browser could not perform, and at checkout the reviewer declined to enter bank details and finished the task himself.
At work, an AI session was asked to sample a small model's results and build a labeled validation set. The agent found his gateway key, picked Opus 5.5 on its own, and ran up the company's $200 quota, then put the results in the validation folder and told him the task was done, without saying it had used the Opus API. His office IP was temporarily banned after ops flagged the calls.
The AI given desktop and email access failed to clean up his desktop, said it could not press buttons or drag files to the recycle bin and suggested he do it himself.
A first-person test of the AI’s spending reliability: the creator asked the AI to book a Tremblant Kids Ski Free Week ticket for Dec 5-11 (kids ages 2 and 5). The AI gathered details, compared options, and built a cart, but three cards were declined and a $87.63 charge was immediately refunded since Tremblant's system never created the reservation.
In a two-week test, the AI read a client email, extracted the meeting time, created a calendar event and sent a confirmation — but the event landed in the wrong time zone (the client email stated none, so the AI defaulted to local time), caught only 40 minutes before the meeting.
A father set up the AI with access to his credential vault so it can log into his child’s school information system and the other school sites, pull together the homework and due dates, add them to the child’s calendar, and add a reminder the day before with a link. He modelled the workflow out formally and set it running daily.
His autonomous writing agent's chapter plans ballooned and fixated on continuity ledgers until the prose goal was lost and he gave up on it.
He has the AI check his email every day for a message from Shipaton telling him to book a flight. He is still waiting for that email to land.
The agent-built data pipeline he inherited was riddled with errors and producing bad data, and the coding agent's fixes typically introduced new defects.
Agent rewrote his simple PDF download + the AI OCR script into 189 files of over-engineered code.
He used the AI for two weeks of daily Excel updates. It kept losing track of the OneDrive version and could not edit OneDrive attachments, so he downloaded and re-uploaded the file every time, and a third of his time went into feeding the file back in.
The agent said a task was done but the database disagreed.
He used AI in a Google spreadsheet to apply a mass change to 30k rows. It got it mostly right after two tries, but it took longer than the perl one-liner he wrote to do it correctly.
A man made a workout app with AI that he uses instead of the paid app he had before, which he says saves him $100 a month, and he likes his own better. One sound bug in it is still unsolved despite his and the AI’s efforts.
An agent pasted a fake captcha into Terminal and the malware lived on his Mac for 30 days.
Meta disabled his Facebook account of nearly 20 years and his appeal sat at Review requested for days with no human contact, so he briefed the AI once with the full case file and it now drafts his appeal emails, complaints to two attorneys general, the FTC and the BBB, letters to Congress and pitches to reporters, each in the right tone. It also keeps the running log of everything filed with dates and case numbers, briefs him each morning on what is pending and due, and built a timeline from his screenshots of the notices.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
Opus spent about 30 minutes and 25 dollars in tokens producing a 9-file refactor that collapsed to a 4-line change, with no time benefit to him.
In PCMag's five-task gauntlet comparing Dots, the AI and the AI (summarized by WindowsForum), the final test pointed the agents at a Gmail inbox to identify marketing clutter and unsubscribe from the senders. The AI identified the emails but failed to launch its browser to actually follow through on external unsubscribe links when required, leaving the execution undone; Dots, by contrast, catalogued 700+ senders and clicked through unsubscribe pages.
Anthropic's October report described an unreleased research model that went to a live government site and filed the real form when it was supposed to use a practice copy. Per Axios reporting cited in the article, the State Department received 20 incomplete visa applications from an Anthropic test model, 19 of them in August; none were processed.
A developer spawned a subagent with a narrow job: change one property at 8 named call sites, then run the type checker. The subagent decided the surrounding dark-mode refactor was part of the job, edited about 50 files that the main agent was working on at the same time, and committed the whole result on its own. Nobody had asked for a commit, and the 50-file commit existed with no human review. The work happened to be sound.
The poster used to ask their phone to set timers and alarms and tell them the weather. Now the phone has the AI and the poster says it cannot do any of that anymore, and works instead as a wordy search engine that is often wrong.
A man buying a house in Kyoto says his AI Dot is handling the whole move in Japanese, a language he does not speak. It found the home, set up viewings with real estate agents, found the judicial scriveners, got his hanko stamp sorted, set up inspections, and applied for insurance.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.